Overview

Study area

GPS data

Building a hiking network

Results

Conclusions

Collected using QStarz Q1100P GPS Tracking Recorders between 2016 and 2018

40+ hour battery life and simple use

Location recorded every 30 seconds

Scientists turned devices on when leaving base camp in the morning and off on return at the end of the day

Building a hiking network

Start with a regular lattice across the study area

Many topologies could be used

We went with hexagonal at ~100m spacing

Hiking functions

Smoothing GPS data

Curve fitting to cleaned data

Terrain-dependent vs combined

Completing and applying the network

Assign edges estimated traversal times based on slope, the hiking function, and land cover (moraine or rock)

Make the nodes and edges into a directed graph

Then use various graph algorithms to find, e.g., everywhere-to-everywhere shortest paths

Betweenness centrality

After some exploration, we settled on betweenness centrality as a graph metric that might be useful

This measure counts how many times each node appears on the shortest paths between every other pair of nodes

An indicator of the relative likelihood of each location being visited

Radius-limited betweenness

Restrict betweenness centrality to nodes no more than some time apart

This is much faster to calculate (yay!)

Also… it looks like it has more value

It (perhaps) is relevant to how people navigate in such environments

Planning a path network?

One way to minimise impact might be to plan paths

This is experimental at this stage

Based on a minimum spanning tree approximation to an arborescence

Conclusions

Terrain-differentiated data-fitted hiking functions are a novelty

Potential wider application of radius-limited betweenness centrality?

A manuscript almost finished

 

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